...Gianmarco Lorenti; Ivan Mariuzzo; Francesco Moraglio; Maurizio Repetto Purpose This paper aims to compare stochastic gradient method used for neural network training with global optimizer without use of gradient information, in particular differential evolution. Design/methodology/approach...

... for parameters of Meta-ELM, M = 15 − 20 and N_h = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm...

...Saddam Bensaoucha; Sid Ahmed Bessedik; Aissa Ameur; Ali Teta Purpose The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs...

...Min Li; Arber Caushaj; Rodrigo Silva; David Lowther Purpose This paper aims to presents a novel application of neural network (NN) pattern recognition to ore rock sorting using inductive electromagnetic (EM) sensors. Design/methodology/approach The impedance of a metallic rock can be measured...

...@iem.pw.edu.pl © Emerald Group Publishing Limited 2016 Noise Sensors Signal processing Neural networks Support vector machine Differential nose Random forest Noisy measurement The electronic noses applying the semiconductor sensors are very popular in recognition of aroma (Cheng et...

..., results of basalt fiber reinforced composite materials inspection and identification using artificial neural networks is presented and discussed. Findings – It is possible to develop ADR system for non-destructive evaluation of dielectric materials using pulsed terahertz technique. New set...

... – Computational fluid dynamics (CFD) has been used to include a wide range of different designs, operating conditions and cooling schemes to ensure accurate results for a wide range of possible machines. Neural networks are used to correlate the computed heat transfer coefficients to various design parameters...

... Publishing Limited 2014 Extended generalized Lambda distribution Independent components analysis Neural networks Short-term forecasting The noise signals are typically modeled in stochastic processes approach as a sequence of independent and identically distributed random variables...

... has a five-level cascaded H-bridge multilevel inverter (CHBMLI) as a harmonic filter. The control scheme includes one neural network controller and two fuzzy logic-based controllers for harmonic extraction, dc capacitor voltage balancing, and compensating current adjustment, respectively. The topology...

...Salvatore Coco; Antonino Laudani; Francesco Riganti Fulginei; Alessandro Salvini Purpose – This paper aims the application of a novel synergy between a neural network (NN) and the finite element method (FEM) in the solution of electromagnetic problem involving hysteretic material in unbounded...

... is used to estimate the main geometric parameters. This does not work for many devices, particularly where eddy currents and non-linearity dominate. The purpose of this paper is to investigate an approach using a neural network trained on a large database of existing designs as a general sizing system...

...Miklós Kuczmann; Amália Iványi On the basis of the Kolmogorov‐Arnold theory, the feedforward type artificial neural networks (NNs) are able to approximate any kind of nonlinear, continuous functions represented by its discrete set of measurements. A NN‐based scalar hysteresis model has been...

... dependence, accommodation, and so on. Artificial neural networks (NNs) are widely used in fields of research where the solution of problems with conventional methods on traditional computers is very difficult to work out, for example system identification, modeling and function approximation. NNs can...

...Stanislaw Osowski; Robert Salat The paper presents the application of self‐organizing neural network for the location of the fault in the transmission line and estimation of the parameter of the faulty element. The location of fault is done on the basis of the measurement of some node voltages...

... electromagnetic design and optimisation are presented and compared. The main features and performance of each are described and evaluated on an analytical and a physical problem. © MCB UP Limited 2001 Electromagnetics Design Fuzzy systems Neural networks Evolution strategies Optimisation...

...) is performed by approximating the corresponding electromagnetic signal by a neural network. Investigations on a ferrous conductive rod will be described in the paper. © MCB UP Limited 2001 Eddy currents Finite element Inverse problems Neural networks Optimization To identify unknown...

.... In this paper new parallel algorithms are proposed, which can be implemented by analogue adaptive circuits employing some neural networks principles. Algorithms based on the least‐squares (LS) and the total least‐squares (TLS) criteria are developed and compared. The problems are formulated as optimization...

...) or the boundary element method (BEM). One approach to reduce the number of FEM or BEM calls using neural networks and another one using multiquadric functions have been introduced recently. This paper compares the efficiency of both methods, which are applied to a couple of test problems and the results...

...J. Seguin; F. Dandurand; D.A. Lowther; J.K. Sykulski The paper presents a novel method of utilising neural networks for optimisation systems. First, a conventional magnetic circuit model of the device is developed to create a set of sensitivity rules which guide the optimisation. The rules...

... regime for the magnetiser. This is usually done by solving a set of coupled ordinary differential equations describing current waveforms and the temperature rise in the magnetising winding. The neurocontroller is based on a one‐layer feedforward neural network which is trained using the Levenberg...